Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
日本語の概要は準備中です。原文の説明を表示しています。
Use when deciding which aeja-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an American Economic Journal: Applied Economics (AEJ: Applied) submission. Routes — it does not replace — the specialized skills.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
This is the router. It tells you which aeja- skill to use at the current stage* of a manuscript aimed at the American Economic Journal: Applied Economics (AEJ: Applied) — the American Economic Association's quarterly journal for empirical applied microeconomics with credible causal identification: labor, development, health, education, public, urban, environmental, and household finance. AEJ: Applied is empirical-first: a clean research design carrying a substantive economic question, with theory used to interpret and structure rather than to lead.
Default assumption: unless the user says otherwise, treat the target as AEJ: Applied. Operational tells that you are at AEJ: Applied and not a sibling: review is single-blind (author identities are visible to referees); submission is via the AEA ScholarOne system with fee tiers by AEA membership and country-income status; JEL codes and a 100-word abstract are required; and the signature stable differentiator is the AEA Data and Code Availability Policy — administered by the AEA Data Editor (Lars Vilhuber), accepted papers deposit data + code to the AEA Data and Code Repository on openICPSR, and the package is checked for reproducibility before publication. On 2026-06-20 the editor page listed Raymond Fisman (Boston University) as Editor, with coeditors Leah Boustan, Marika Cabral, Peter Hull, Xavier Jaravel, and Nicholas Ryan. Check live AEA pages for fees and editors before submission-week advice.
| Current symptom | Next skill |
|---|---|
| Unsure the paper fits AEJ: Applied vs AER / AEJ: Policy / a field journal | aeja-topic-selection |
| Contribution vs. the literature is fuzzy or undersold | aeja-literature-positioning |
| Causal design rests on OLS+controls, TWFE on staggered timing, or a weak IV | aeja-identification |
| A model is needed to interpret estimates or structure a mechanism | aeja-theory-model |
| Results not shown robust to specification, sample, inference | aeja-robustness |
| Exhibits are dense; main result not legible in one table/figure | aeja-tables-figures |
| Prose buries the design; abstract/intro do not land | aeja-writing-style |
| Data/code deposit, README, openICPSR prep for the AEA Data Editor | aeja-replication-package |
| Want to anticipate referee objections before submitting | aeja-referee-strategy |
| Ready to submit via the AEA portal; need a preflight | aeja-submission |
| Received an R&R; need a response-letter strategy | aeja-rebuttal |
aeja-topic-selection — lock the applied-micro question and venue fitaeja-literature-positioning — stake the marginal contributionaeja-identification — make the data-to-causal-estimate mapping credibleaeja-theory-model — add only the theory that interprets/structures the estimateaeja-robustness — show the headline survives specification, sample, inferenceaeja-tables-figures — make the main result legible in one exhibitaeja-writing-style — make the design and estimate land (abstract + intro last)aeja-replication-package — assemble the openICPSR package for the AEA Data Editoraeja-referee-strategy — pre-empt the objections this design invitesaeja-submission — AEA portal preflight (single-blind front matter, JEL, fee)aeja-rebuttal — after the R&R
aeja-writing-styleis a late-stage polish; do not rewrite the intro before identification and robustness settle.
aeja-replication-package)aeja-tables-figures) while the identification or headline estimate is still movingAEJ: Applied spans several applied-micro designs, and the first bottleneck differs by design. Read the archetype, then enter the chain at the right link.
| Archetype | Likely first bottleneck | Enter at |
|---|---|---|
| field experiment / RCT (own data) | pre-registration, balance, attrition, estimand | aeja-identification → aeja-robustness |
| quasi-experiment (DID / event study) | staggered-timing bias, parallel-trends credibility | aeja-identification |
| regression discontinuity | density manipulation, bandwidth, bias-corrected CIs | aeja-identification → aeja-robustness |
| IV / shift-share | first-stage strength, exclusion, exposure design | aeja-identification |
| descriptive / measurement with a clean question | framing the contribution without a causal overclaim | aeja-topic-selection → aeja-literature-positioning |
A user says: "My event study on a staggered state policy looks fine, but a referee says the two-way fixed-effects estimate is contaminated and the parallel-trends story is thin." That is two AEJ: Applied pushbacks — negative-weighting / contaminating already-treated comparisons under staggered timing and pre-trend credibility — both owned by aeja-identification, with the robustness presentation in aeja-robustness. Route there first; only once the heterogeneity-robust estimate is stable (say it settles at a 3.1pp effect, s.e. 0.9, illustrative) do you return to aeja-tables-figures and aeja-rebuttal to present and defend it.
if decision_letter_arrived: -> aeja-rebuttal
elif ready_to_submit: -> aeja-submission
elif anticipating_referees: -> aeja-referee-strategy
elif building_replication_package: -> aeja-replication-package
elif intro_or_abstract_weak: -> aeja-writing-style
elif exhibits_dense: -> aeja-tables-figures
elif results_not_robust: -> aeja-robustness
elif needs_model_to_interpret: -> aeja-theory-model
elif identification_shaky: -> aeja-identification
elif contribution_fuzzy: -> aeja-literature-positioning
else: -> aeja-topic-selection
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
日本語の概要は準備中です。原文の説明を表示しています。
Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.
日本語の概要は準備中です。原文の説明を表示しています。
Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.
日本語の概要は準備中です。原文の説明を表示しています。
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
日本語の概要は準備中です。原文の説明を表示しています。
Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.
日本語の概要は準備中です。原文の説明を表示しています。
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
日本語の概要は準備中です。原文の説明を表示しています。